Talend Big Data Integration Training Course

This instructor-led live training in South Korea (online or onsite) targets intermediate-level data scientists and engineers who want to apply Google Colab and Apache Spark for big data processing and analytics.

By the conclusion of this training, participants will be able to:

• Establish a big data environment using Google Colab and Spark.
• Process and analyze large datasets efficiently with Apache Spark.
• Visualize big data in a collaborative environment.
• Integrate Apache Spark with cloud-based tools.

Apache Hadoop is the leading framework for processing Big Data across server clusters. Over the course of three (or four, optional) days, participants will explore the business value and use cases of Hadoop and its ecosystem, learn to plan for cluster deployment and scalability, and master the installation, maintenance, monitoring, troubleshooting, and optimization of Hadoop. Attendees will also gain practical experience with bulk data loading, become familiar with various Hadoop distributions, and practice installing and managing ecosystem tools. The course concludes with a discussion on securing clusters using Kerberos.

“...The materials were very well prepared and covered thoroughly. The Lab was very helpful and well organized.”
— Andrew Nguyen, Principal Integration DW Engineer, Microsoft Online Advertising

Audience

Hadoop administrators

Format

Lectures combined with hands-on labs, with an approximate balance of 60% lectures and 40% labs.

Apache NiFi is an open-source platform for data integration and event processing that utilizes a flow-based architecture. It facilitates the automated, real-time routing, transformation, and mediation of data between diverse systems, featuring a web-based user interface and granular control capabilities.

This instructor-led live training, available either onsite or remotely, is designed for intermediate-level administrators and engineers who aim to deploy, manage, secure, and optimize NiFi dataflows within production environments.

Upon completing this training, participants will be capable of:

• Installing, configuring, and maintaining Apache NiFi clusters.
• Designing and managing dataflows originating from various sources and destinations.
• Implementing logic for flow automation, routing, and transformation.
• Optimizing performance, monitoring operations, and resolving issues.

Course Format

• Interactive lectures accompanied by discussions on real-world architecture.
• Practical labs focused on building, deploying, and managing dataflows.
• Scenario-based exercises conducted in a live-lab environment.

Course Customization Options

• For inquiries regarding customized training for this course, please reach out to us to arrange details.

In this instructor-led live training in South Korea, participants will learn the fundamentals of flow-based programming as they develop a number of demo extensions, components and processors using Apache NiFi.

By the end of this training, participants will be able to:

• Understand NiFi's architecture and dataflow concepts.
• Develop extensions using NiFi and third-party APIs.
• Custom develop their own Apache Nifi processor.
• Ingest and process real-time data from disparate and uncommon file formats and data sources.

This training offers a hands-on introduction to developing scalable data processing and Machine Learning workflows with PySpark. Participants will gain insights into how Apache Spark functions within contemporary Big Data ecosystems and learn to efficiently handle large datasets by applying distributed computing principles.

This instructor-led, live training in South Korea (online or onsite) is designed for engineers who wish to set up and deploy Apache Spark systems for processing very large amounts of data.

By the end of this training, participants will be able to:

• Install and configure Apache Spark.
• Quickly process and analyze very large data sets.
• Understand the difference between Apache Spark and Hadoop MapReduce and when to use which.
• Integrate Apache Spark with other machine learning tools.

This instructor-led live training in South Korea (online or on-site) targets beginner to intermediate-level system administrators looking to deploy, maintain, and optimize Spark clusters.

By the end of this training, participants will be able to:

• Install and configure Apache Spark in various environments.
• Manage cluster resources and monitor Spark applications.
• Optimize the performance of Spark clusters.
• Implement security measures and ensure high availability.
• Debug and troubleshoot common Spark issues.

While the initial learning curve for Apache Spark can be steep, requiring significant effort to see early results, this course is designed to help you quickly overcome that initial hurdle. By the end of the training, participants will grasp the fundamentals of Apache Spark, clearly distinguish between RDDs and DataFrames, and gain proficiency with both Python and Scala APIs. You will also develop a solid understanding of executors, tasks, and other core components. Additionally, the course emphasizes best practices for cloud deployment, with a strong focus on Databricks and AWS. Students will explore the differences between AWS EMR and AWS Glue, one of AWS's newer Spark services.

AUDIENCE:

Data Engineers, DevOps Professionals, Data Scientists

OBJECTIVE:

This course provides an introduction to Apache Spark. Participants will explore how Spark integrates into the Big Data ecosystem and gain practical skills in using Spark for data analysis. The curriculum covers the Spark shell for interactive analysis, Spark internals, various Spark APIs, Spark SQL, Spark Streaming, as well as Machine Learning and GraphX.

AUDIENCE :

Developers and Data Analysts

This instructor-led, live training in South Korea (online or onsite) is designed for data scientists and developers who intend to use Spark NLP, built on top of Apache Spark, to develop, implement, and scale natural language text processing models and pipelines.

By the end of this training, participants will be able to:

• Set up the necessary development environment to start building NLP pipelines with Spark NLP.
• Understand the features, architecture, and benefits of using Spark NLP.
• Use the pre-trained models available in Spark NLP to implement text processing.
• Learn how to build, train, and scale Spark NLP models for production-grade projects.
• Apply classification, inference, and sentiment analysis on real-world use cases (clinical data, customer behavior insights, etc.).

In this instructor-led, live training in South Korea, participants will learn how to use Python and Spark together to analyze big data as they work on hands-on exercises.

By the end of this training, participants will be able to:

• Learn how to use Spark with Python to analyze Big Data.
• Work on exercises that mimic real world cases.
• Use different tools and techniques for big data analysis using PySpark.

This instructor-led, live training in South Korea (online or onsite) is aimed at developers who wish to use and integrate Spark, Hadoop, and Python to process, analyze, and transform large and complex data sets.

By the end of this training, participants will be able to:

• Set up the necessary environment to start processing big data with Spark, Hadoop, and Python.
• Understand the features, core components, and architecture of Spark and Hadoop.
• Learn how to integrate Spark, Hadoop, and Python for big data processing.
• Explore the tools in the Spark ecosystem (Spark MlLib, Spark Streaming, Kafka, Sqoop, Kafka, and Flume).
• Build collaborative filtering recommendation systems similar to Netflix, YouTube, Amazon, Spotify, and Google.
• Use Apache Mahout to scale machine learning algorithms.

Spark SQL serves as Apache Spark's dedicated module for processing both structured and unstructured data. It offers insights into data structure and the computations involved, enabling the engine to apply performance optimizations effectively. Spark SQL is commonly utilized for executing SQL queries and accessing data from existing Hive installations.

In this instructor-led live training, available either onsite or remotely, participants will gain the skills necessary to analyze diverse data sets using Spark SQL.

Upon completion of this training, participants will be equipped to:

• Install and configure Spark SQL.
• Conduct data analysis leveraging Spark SQL.
• Query data sets across various formats.
• Visualize data alongside query results.

Course Format

• Interactive lectures and discussions.
• Extensive exercises and practical application.
• Hands-on implementation within a live laboratory environment.

Course Customization Options

• For customized training requests, please contact us to arrange.

Stratio offers a data-centric platform that seamlessly integrates big data, artificial intelligence, and governance into a unified solution. Its Rocket and Intelligence modules empower organizations to perform rapid data exploration, transformation, and advanced analytics within enterprise settings.

This instructor-led live training, available both online and onsite, targets intermediate-level data professionals looking to effectively utilize Stratio's Rocket and Intelligence modules with PySpark. The curriculum emphasizes looping structures, user-defined functions (UDFs), and complex data logic.

Upon completion of this training, participants will be equipped to:

• Navigate and operate within the Stratio platform using the Rocket and Intelligence modules.
• Apply PySpark techniques for data ingestion, transformation, and analysis.
• Utilize loops and conditional logic to manage data workflows and execute feature engineering tasks.
• Develop and manage user-defined functions (UDFs) to enable reusable data operations within PySpark.

Course Format

• Engaging lectures and interactive discussions.
• Numerous exercises and hands-on practice sessions.
• Practical implementation exercises in a live laboratory environment.

Customization Options

• For tailored training requests, please contact us to arrange specific requirements.